Model Adaptation - Edgentiq

The Fading Footprints: How Fine-Tuning Impacts Knowledge Edits in Language Models

Self-Harmony: A New Framework for Robust AI Reasoning at Test Time

EdgeSync: Keeping Edge AI Models Sharp Amidst Changing Real-World Conditions

VESSA: Adapting Vision Models with Object-Centric Videos

Enhancing Model Adaptation with Alpha-LoRA: A New Fine-Tuning Approach

spot_img

Recently Added

Enhancing LLM Adaptation Through Reflective Memory Systems

Read more

Unlocking Adaptability: New Benchmark for Editing Auditory Knowledge in AI Models

Read more

Strategies for Skill Acquisition in Large Multimodal Models

Read more

TensLoRA: Enhancing Low-Rank Adaptation with Tensor-Based Approaches

Read more

Enhancing ECG Foundation Models with a Targeted Post-Training Strategy

Read more

A Smarter Approach to Fine-Tuning Large AI Models: Introducing IPA

Read more

Bridging Eras: Advanced AI for Historical Portrait Identification

Read more

TransMiter: A New Approach to Efficiently Adapt Large Vision-Language Models

Read more

Smart LLM Adaptation: DP-LLM Adjusts Precision on the Fly

Read more

Unlocking Vision-Language Models: A Deep Dive into Label-Free Adaptation

Read more

Enhancing Multi-Task Learning in Transformers Through Dynamic Token Adjustments

Read more

Gen AI News and Updates

spot_img

- Advertisement -

The Fading Footprints: How Fine-Tuning Impacts Knowledge Edits in Language Models

November 11, 2025

Self-Harmony: A New Framework for Robust AI Reasoning at Test Time

November 4, 2025

EdgeSync: Keeping Edge AI Models Sharp Amidst Changing Real-World Conditions

October 28, 2025